Use of lidar-derived landscape parameters to characterize alternative harvest system options in the Inland Northwest. Issue 3 (2nd September 2018)

• Record Type: Journal Article
• Authors: Becker, Ryer M.; Keefe, Robert F.; Anderson, Nathaniel M.; Eitel, Jan U.H.
• Is Part Of: International journal of forest engineering. Volume 29:Issue 3(2018)
• Page Start: 179
• Page End: 191
• Publication Date: 2018-09-02
• Subjects: Lidar -- logging systems -- logging -- decision support -- precision forestry; Forestry engineering -- Periodicals; Génie forestier -- Périodiques; Forestry engineering; Periodicals; 634.905
• DOI: 10.1080/14942119.2018.1497255
• Languages: English
• ISSNs: 1913-2220

Abstract

ABSTRACT: As innovative harvest systems are developed, the extent to which they can be utilized on the landscape based on machine capabilities is often unclear to forest managers. Spatial decision support models may aid contractors and forest planners in choosing appropriate logging systems based on topography and stand characteristics. Lidar and inventory data from 91 sample plots were used to model site characteristics for 2627 stands in the Slate Creek drainage on the Nez Perce Clearwater National Forest in north-central Idaho, USA, and were integrated into a decision support model to compare harvest system selection using five harvest systems and three scenarios. In two of the scenarios, shovel harvester-based logging systems, which are not common in the area, were included to determine potential sites where integration of these systems is possible based on landscape and stand conditions. Lidar-derived predictions for volume and trees per hectare were determined with model accuracies of 76.4% and 70.3%, and together with topographic characteristics it was determined that shovel harvester-based options were feasible across a significant portion of the study area (31% and 34% in the two scenarios). Additionally, increasing operable slope for ground-based systems by 10% increased the area in harvestable classification by 21%. Harvest system classification using lidar-derived products and known system capabilities allows contractors and managers to better evaluate alternative harvest system options on landscape scales and may encourage the utilization of innovative machinery not currently integrated into most logging operations.